{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fbc2d16-59f9-4be3-b93e-1a5440c7efd0",
   "metadata": {},
   "source": [
    "# Tutorial 2 - Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afe6a1e-3ab4-4f3f-ad47-f6cd66419504",
   "metadata": {},
   "source": [
    "After working with a regression example in the first tutorial, we proceed with a classification example in this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a2ef2a6-f681-427f-8252-ade2111ce0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jaxkan.KAN import KAN\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "from flax import nnx\n",
    "import optax\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e70a5d-0340-4bdc-af4e-150d0098a87e",
   "metadata": {},
   "source": [
    "## Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166ad90d-430e-45ca-86a8-e6e4dbc1c943",
   "metadata": {},
   "source": [
    "We will use the `make_classification` method of `sklearn.datasets` to generate some mock data for the classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b986e75a-6d4a-402f-bea7-36d13f4a7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate synthetic data\n",
    "seed = 42\n",
    "\n",
    "X, y = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=2,\n",
    "    n_informative=2,\n",
    "    n_redundant=0,\n",
    "    n_clusters_per_class=1,\n",
    "    class_sep=1.5,\n",
    "    random_state=seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db8bf4bc-49fe-4014-94aa-4e5dbf632369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the generated data\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], label='Class 0', alpha=0.7, marker='x', color='#a3630f')\n",
    "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], label='Class 1', alpha=0.7, marker='o', color='#25599c')\n",
    "plt.title(\"Synthetic Data for Binary Classification\")\n",
    "plt.xlabel(\"Feature 1\")\n",
    "plt.ylabel(\"Feature 2\")\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485197f9-2e41-481e-a2cd-977452622ff3",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26fac97-6716-47ff-b8db-824df7c6aae2",
   "metadata": {},
   "source": [
    "We split the data in train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad91a42a-ffe8-4b8c-b1a7-cc9b60dfe5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: (800, 2)\n",
      "Test set size: (200, 2)\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1, 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)\n",
    "\n",
    "print(\"Training set size:\", X_train.shape)\n",
    "print(\"Test set size:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9d9bba-71e2-4d5b-95b1-36167fb70c1a",
   "metadata": {},
   "source": [
    "## KAN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd54c6c-3d28-4864-af38-3b22875d0f0a",
   "metadata": {},
   "source": [
    "We covered KAN Model selection in the first tutorial, so feel free to refer to it for more info. For this example, we will be using a Fourier KAN Layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b350b56f-5daa-411f-9090-b544760c34ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;79;201;177mKAN\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 1,093 (4.4 KB)\u001b[0m\n",
      "  \u001b[38;2;156;220;254mlayer_type\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;207;144;120m'fourier'\u001b[0m,\n",
      "  \u001b[38;2;156;220;254mlayers\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m[\u001b[0m\u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 246 (984 B)\u001b[0m\n",
      "    \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 6 (24 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 120 (480 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 120 (480 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m\n",
      "  \u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 726 (2.9 KB)\u001b[0m\n",
      "    \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 6 (24 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 360 (1.4 KB)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 360 (1.4 KB)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m\n",
      "  \u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 121 (484 B)\u001b[0m\n",
      "    \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray([0.], dtype=float32)\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 60 (240 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 60 (240 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m\n",
      "  \u001b[38;2;255;213;3m)\u001b[0m\u001b[38;2;255;213;3m]\u001b[0m\n",
      "\u001b[38;2;255;213;3m)\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Initialize a KAN model\n",
    "n_in = X_train.shape[1]\n",
    "n_out = y_train.shape[1]\n",
    "n_hidden = 6\n",
    "\n",
    "layer_dims = [n_in, n_hidden, n_hidden, n_out]\n",
    "req_params = {'D': 10}\n",
    "\n",
    "model = KAN(layer_dims = layer_dims,\n",
    "            layer_type = 'fourier',\n",
    "            required_parameters = req_params,\n",
    "            seed = seed\n",
    "           )\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f681d135-c885-4e59-87d7-1ea16a157c50",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6723a412-c313-453c-aa88-9274687ee54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_type = optax.adam(learning_rate=0.001)\n",
    "\n",
    "optimizer = nnx.Optimizer(model, opt_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaac2ddc-ad53-40db-804b-7f54f3000476",
   "metadata": {},
   "source": [
    "In this case, the loss function is modified to correspond to a cross entropy loss term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f18ab849-3c05-418a-a30b-3928f77c332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define train loop\n",
    "@nnx.jit\n",
    "def train_step(model, optimizer, X_train, y_train):\n",
    "\n",
    "    def loss_fn(model):\n",
    "\n",
    "        logits = model(X_train)\n",
    "        probs = nnx.sigmoid(logits)\n",
    "        loss = jnp.mean(-y_train * jnp.log(probs + 1e-8) - (1 - y_train) * jnp.log(1 - probs + 1e-8))\n",
    "\n",
    "        return loss\n",
    "    \n",
    "    loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
    "    optimizer.update(grads)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b06282a5-8899-4801-9c9e-d8b73b55d74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize train_losses\n",
    "num_epochs = 2000\n",
    "train_losses = jnp.zeros((num_epochs,))\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Calculate the loss\n",
    "    loss = train_step(model, optimizer, X_train, y_train)\n",
    "    \n",
    "    # Append the loss\n",
    "    train_losses = train_losses.at[epoch].set(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faab949e-dadb-4cec-bc13-63b10cb609c5",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86630d29-9b9f-452a-b2f1-399b02768829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "\n",
    "plt.plot(np.array(train_losses), label='Train Loss', marker='o', color='#25599c', markersize=1)\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss Over Epochs')\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef7d214-6efa-4124-a587-b47e98cac7d6",
   "metadata": {},
   "source": [
    "Now the model is evaluated in terms of its F1-Score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7798eb68-d6b5-47ec-b845-cf3d60dccf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The F1-Score of the fit is 93.194%\n"
     ]
    }
   ],
   "source": [
    "logits = model(X_test)\n",
    "y_pred = np.array((nnx.sigmoid(logits) > 0.5).astype(int))\n",
    "score = f1_score(y_pred, y_test)\n",
    "\n",
    "print(f\"The F1-Score of the fit is {100*score:.3f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88cc341a-8869-4dd9-be77-ef9bf2d8b5c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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